Senior Machine Learning Engineer (GCP)
Tiger Analytics Inc. Open · verified Oct 24, 2026 At a glance
Senior Machine Learning Engineering role at Tiger Analytics Inc. Remote, Canada · no salary stated · we estimate CA$139,400–CA$200,000 across Canada.
Tiger Analytics Inc. has not published a salary for this role. The figure below is our estimate, not theirs.
remoteroles summary, based on the employer's posting.
What you'll do
- Build and tune models in Vertex AI, using pipelines, AutoML or custom training as the work requires
- Create ML workflows that carry features through training and evaluation into deployment
- Put models on Vertex AI endpoints and connect them to applications or APIs
- Track model quality, then configure alerts, drift checks and retraining triggers
- Apply CI/CD, governance and security practices, and document model workflows for internal stakeholders
What you bring
- You bring advanced generative AI skills, including hybrid Graph-based RAG, multimodal agents, agent frameworks, fine-tuning and distillation
- You write production Python with strong object-oriented and functional skills, plus ML library experience and testing or optimization work
- You can design with GCP services such as Vertex AI, BigQuery and Cloud Storage, and understand IAM and VPC
- You have built REST APIs for real-time model inference, including authentication, logging and performance improvements
- You can architect scalable AI systems and have hands-on experience with distributed services and asynchronous processing
Who this fits
This senior role suits someone who can take ML work from data ingestion through model serving and monitoring, while working with data science, data engineering and MLOps teams. It is listed as remote in Canada.
From the employer
Tiger Analytics is looking for a skilled and innovative Machine Learning Engineer with hands-on experience in Google Cloud Platform (GCP) and Vertex AI to design, build, and deploy scalable ML solutions. You will play a key role in operationalizing machine learning models and driving the end-to-end ML lifecycle, from data ingestion to model serving and monitoring.
Key Responsibilities:
- Develop, train, and optimize ML models using Vertex AI, including Vertex Pipelines, AutoML, and custom model training.
- Design and build scalable ML pipelines for feature engineering, training, evaluation, and deployment.
- Deploy models to production using Vertex AI endpoints and integrate with downstream applications or APIs.
- Collaborate with data scientists, data engineers, and MLOps teams to enable reproducible and reliable ML workflows.
- Monitor model performance and set up alerting, retraining triggers, and drift detection mechanisms.
- Utilize GCP services such as BigQuery, Dataflow, Cloud Functions, Pub/Sub, and GCS in ML workflows.
- Apply CI/CD principles to ML models using Vertex AI Pipelines, Cloud Build, and GitOps practices.
- Implement model governance, versioning, explainability, and security best practices within Vertex AI.
- Document architecture decisions, workflows, and model lifecycle clearly for internal stakeholders.
Requirements
1. Advanced Generative AI
- Advanced RAG including Graph based hybrid retrieval
- Multimodal agent
- Deep knowledge on ADK , Langchain Agentic Frameworks
- Fine tuning and Distillation
2. Python Expertise
- Expert in Python with strong OOP and functional programming skills
- Proficient in ML/DL libraries: TensorFlow, PyTorch, scikit-learn, pandas, NumPy, PySpark
- Experience with production-grade code, testing, and performance optimization
3. GCP Cloud Architecture & Services
- Proficiency in GCP services such as:
- Vertex AI
- BigQuery
- Cloud Storage
- Cloud Run
- Cloud Functions
- Pub/Sub
- Dataproc
- Dataflow
- Understanding of IAM, VPC
6. API Development & Integration
- Designs and builds RESTful APIs using FastAPI or Flask
- Integrates ML models into APIs for real-time inference
- Implements authentication, logging, and performance optimization
7. System Design & Scalability
- Designs end-to-end AI systems with scalability and fault tolerance in mind
- Hands-on experience in developing distributed systems, microservices, and asynchronous processing
Benefits
This position offers an excellent opportunity for significant career development in a fast-growing and challenging entrepreneurial environment with a high degree of individual responsibility.
Machine Learning Engineering work from LinkedIn, X, Reddit and Upwork, in one list.
Each morning you get everything that fits, strongest first.
Machine Learning EngineeringRemote
Find me workBy the team behind remoteroles.